---
title: "AMPs ARGENTINA"
output:
flexdashboard::flex_dashboard:
theme: lumen
social: menu
source: embed
logo: logo.png
---
```{r setup, include=FALSE}
library(flexdashboard)
library(leaflet)
library(ggplot2)
library(plotly)
library(spocc)
library(readr)
library(reshape2)
library(xts)
library(dygraphs)
library(plotly)
library(lubridate)
library(dplyr)
library(htmltools)
library(DT)
library(shiny)
library(xts)
library(htmlwidgets)
library(RColorBrewer)
palette(brewer.pal(8, "Set2"))
```
```{r dataread, message=FALSE, warning=FALSE, include=FALSE}
#READ both files metadata and percent cover
setwd(paste0(getwd(),"/DATA"))#set new WD to folder DATA
AMP.cover <- read.csv("AMPs_percent_covers.csv")
AMP.metadata <- read.csv("AMPs_metadata.csv")
setwd("..")# original WD
colnames(AMP.cover)[2] <-"Name"
#Merge AMP.metadata and AMP.cover
AMP<- merge(AMP.metadata,AMP.cover, by = "Name", all.x = TRUE)
#all seaweed
AMP$algae <- as.numeric(paste(AMP$MAA +AMP$MAEC + AMP$MAEF+ AMP$MAEN+ AMP$MAF+ AMP$MAG+ AMP$MALA+ AMP$MALCB+ AMP$MAS))
#Create long type dataframe
library(reshape)
AMP_long = melt(AMP, id.vars = 1:21, measure.vars = 22:ncol(AMP), variable_name = "CATAMI", value_name ="cover", na.rm = T)
#rename columns because the ontop command is not working
colnames(AMP_long)[23] <- "cover"
#Calculate mean, SD, SE for cover data by factors
library(doBy)
Coverdata <- summaryBy(cover ~ CATAMI + strata,data=AMP_long, FUN = function(x) { c(mean = mean(x),SD=sd(x),SE = sqrt(var(x)/length(x)))})
#add year from date info
AMP$year <- lubridate::year(AMP$Date)
photo_bydate = as.data.frame(table(AMP$year,AMP$site,AMP$locality,AMP$strata))
colnames(photo_bydate)=c("Fecha","Sitio","Localidad","Estrato","n fotocuadrantes")
#SST
#getSST.r was used to get data
## get sampling event dates
samplingDates = unique(AMP$Date)
## read SST values
library(readr)
setwd(paste0(getwd(),"/DATA"))#set new WD to folder DATA
SST = read_csv("ISLAPINGUINO_SST.csv")
SST.clim = read_csv("ISLAPINGUINO_Climatology.csv")
setwd("..")# original WD
```
Column1 {.tabset .tabset-fade}
-------
### Mapa Fotoquadrantes
```{r map, message=FALSE, warning=FALSE}
library(leaflet)
# Crear el mapa
map <- leaflet(AMP)%>%# add different provider tiles
addProviderTiles(
"Esri.WorldImagery",
# give the layer a name
group = "Satelite"
) %>%
addProviderTiles(
"OpenStreetMap",
group = "Mapa para zoom"
) %>%
# add a layers control
addLayersControl(
baseGroups = c(
"Satelite", "Mapa para zoom"),
# position it on the topleft
position = "topleft"
) %>%# Agregar los iconos de las localidades
addCircleMarkers(
data = AMP,
~Longitude,
~Latitude,
weight = 0.5,
col = 'red',
fillColor = 'red',
radius = 4,
fillOpacity = 0.5,
stroke = T,
popup = ~Comments,
clusterOptions = markerClusterOptions())
map # Mostrar el mapa
```
### Cobertura de organismos vivos por estrato
```{r taxacover}
# Use this when using percent_covers.csv and full names from CoralNet
# taxacover = AMP_long %>% filter(CATAMI != "Substrate..Consolidated..hard.") %>%
# group_by(site, strata, Image.ID) %>%
# summarise(sumcover = sum(cover, na.rm=T))
# Use this when using percent_covers_AMP2023.cvs and short names from CoralNet
taxacover = AMP_long %>% filter(CATAMI != "SC", CATAMI != "algae") %>%
group_by(locality, strata, Image.ID) %>%
summarise(sumcover = sum(cover, na.rm=T))
pp = ggplot(taxacover, aes(x=factor(strata,level=c('LOWTIDE', 'MIDTIDE', 'HIGHTIDE')), sumcover, fill=strata))
pp = pp + geom_boxplot() + ylab("% Cobertura de todas las especies por cuadrante") + xlab("")+
facet_grid(~locality) +
theme_bw(base_size = 10) + theme(legend.position = "none")
ggplotly(pp)
```
### Coberturas de Molluscos
```{r}
#<!-- <img src="images/AMP20053.JPG" width="400px"/> -->
# this creates a gauge with % cover values color coded by threshold levels
library(echarts4r)
# Use this when using percent_covers.csv and full names from CoralNet
# value <- Coverdata[Coverdata$CATAMI == "Molluscs..Bivalves", ]
# Use this when using percent_covers_AMP2023.cvs and short names from CoralNet
value <- Coverdata[Coverdata$CATAMI == "MOB", ]
Molluscs <- e_charts() %>%
e_gauge(round(value$cover.mean[1], 0),
"ALTO",
center = c("20%", "20%"),
radius = "35%",
color = "black",
min=0,
max=100,
splitNumber = 5,
axisLine = list(
lineStyle = list(
color=list(
c(1, "green"),
c(1, "green"),
c(1, "green")
)
))) %>%
e_gauge(round(value$cover.mean[3], 0),
"MEDIO",
center = c("50%", "20%"),
radius = "35%",
color = "black",
min=0,
max=100,
splitNumber = 5,
axisLine = list(
lineStyle = list(
color=list(
c(0.20, "red"),
c(0.4, "yellow"),
c(1, "green")
)
))) %>%
e_gauge(round(value$cover.mean[2], 0),
"BAJO",
center = c("80%", "20%"),
radius = "35%",
color = "black",
min=0,
max=100,
splitNumber = 5,
axisLine = list(
lineStyle = list(
color=list(
c(1, "green"),
c(1, "green"),
c(1, "green")
)
))) %>%
e_title("Moluscos (%)")
Molluscs
# # High tide
# gauge(value$cover.mean[1], min = 0, max = 100, symbol = '%',
# gaugeSectors(success = c(60, 100), warning = c(40, 59), danger = c(0, 39)),
# label = "Estrato alto"
# )
#
# # Mid tide
# gauge(value$cover.mean[3], min = 0, max = 100, symbol = '%',
# gaugeSectors(success = c(60, 100), warning = c(40, 59), danger = c(0, 39)),
# label = "Estrato medio"
# )
#
# # Low tide
# gauge(value$cover.mean[2], min = 0, max = 100, symbol = '%',
# gaugeSectors(success = c(60, 100), warning = c(40, 59), danger = c(0, 39)),
# label = "Estrato bajo"
# )
```
### Coberturas de Macro-Algas
```{r}
# this creates a gauge with % cover values color coded by threshold levels
library(echarts4r)
# Use short names from CoralNet
value <- Coverdata[Coverdata$CATAMI == "algae", ]
MAS <- e_charts() %>%
e_gauge(round(value$cover.mean[1], 0),
"ALTO",
center = c("20%", "20%"),
radius = "35%",
color = "black",
min=0,
max=100,
splitNumber = 5,
axisLine = list(
lineStyle = list(
color=list(
c(1, "green"),
c(1, "green"),
c(1, "green")
)
))) %>%
e_gauge(round(value$cover.mean[3], 0),
"MEDIO",
center = c("50%", "20%"),
radius = "35%",
color = "black",
min=0,
max=100,
splitNumber = 5,
axisLine = list(
lineStyle = list(
color=list(
c(1, "green"),
c(1, "green"),
c(1, "green")
)
))) %>%
e_gauge(round(value$cover.mean[2], 0),
"BAJO",
center = c("80%", "20%"),
radius = "35%",
color = "black",
min=0,
max=100,
splitNumber = 5,
axisLine = list(
lineStyle = list(
color=list(
c(0.10, "red"),
c(.20, "yellow"),
c(1, "green")
)
))) %>%
e_title("Macro-algas (%)")
MAS
```
### Frecuencia de especies
```{r}
taxafreq = AMP_long %>% filter(cover>0)%>%
group_by(locality, strata, CATAMI) %>%
summarise(sppfreq = n()) %>% arrange(sppfreq) %>% mutate(sppacum = cumsum(sppfreq))
pp = ggplot(taxafreq, aes(CATAMI, sppfreq, fill=strata))
pp = pp + geom_bar(stat="identity") + coord_flip() + facet_grid(~locality) +
theme_bw(base_size = 10) + xlab("") + ylab("Número de foto-cuadrantes")
ggplotly(pp)
```
### Categorias más abundantes por estrato
```{r}
# creating a pie chart for each strata with all the CATAMI categories present
sel_data.L <- filter(Coverdata, strata == "LOWTIDE",cover.mean > 0,CATAMI != "algae")
sel_data.M <- filter(Coverdata, strata == "MIDTIDE",cover.mean > 0,CATAMI != "algae")
sel_data.H <- filter(Coverdata, strata == "HIGHTIDE",cover.mean > 0,CATAMI != "algae")
plot_ly(labels=~CATAMI, values=~cover.mean, legendgroup=~CATAMI,textinfo = 'label+percent') %>%
add_pie(data =sel_data.L, name = "Bajo",title = 'Estrato Bajo', domain = list(row = 0, column = 0))%>%
add_pie(data =sel_data.M, name = "Medio",title = 'Estrato Medio', domain = list(row = 0, column = 1))%>%
add_pie(data =sel_data.H, name = "Alto",title = 'Estrato Alto' ,domain = list(row = 0, column = 2))%>%
layout(title = "Categorias CATAMI por estrato", showlegend = T,
grid=list(rows=1, columns=3),
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
```
Column3{data-width=130}
-------
### Fotoquadrantes por fecha y sitio
```{r}
knitr::kable(as.data.frame(table(AMP$year,AMP$locality)),col.names = c("Año","Localidad","n"))
```
